Data Mining_ Concepts and Techniques - Jiawei Han [0]
Cover Image
Front Matter
Copyright
Dedication
Foreword
Foreword to Second Edition
Preface
Acknowledgments
About the Authors
1. Introduction
1.1. Why Data Mining?
1.2. What Is Data Mining?
1.3. What Kinds of Data Can Be Mined?
1.4. What Kinds of Patterns Can Be Mined?
1.5. Which Technologies Are Used?
1.6. Which Kinds of Applications Are Targeted?
1.7. Major Issues in Data Mining
1.8. Summary
1.9. Exercises
1.10. Bibliographic Notes
2. Getting to Know Your Data
2.1. Data Objects and Attribute Types
2.2. Basic Statistical Descriptions of Data
2.3. Data Visualization
2.4. Measuring Data Similarity and Dissimilarity
2.5. Summary
2.6. Exercises
2.7. Bibliographic Notes
3. Data Preprocessing
3.1. Data Preprocessing: An Overview
3.2. Data Cleaning
3.3. Data Integration
3.4. Data Reduction
3.5. Data Transformation and Data Discretization
3.6. Summary
3.7. Exercises
3.8. Bibliographic Notes
4. Data Warehousing and Online Analytical Processing
4.1. Data Warehouse: Basic Concepts
4.2. Data Warehouse Modeling: Data Cube and OLAP
4.3. Data Warehouse Design and Usage
4.4. Data Warehouse Implementation
4.5. Data Generalization by Attribute-Oriented Induction
4.6. Summary
4.7. Exercises
5. Data Cube Technology
5.1. Data Cube Computation: Preliminary Concepts
5.2. Data Cube Computation Methods
5.3. Processing Advanced Kinds of Queries by Exploring Cube Technology
5.4. Multidimensional Data Analysis in Cube Space
5.5. Summary
5.6. Exercises
5.7. Bibliographic Notes
6. Mining Frequent Patterns, Associations, and Correlations
6.1. Basic Concepts
6.2. Frequent Itemset Mining Methods
6.3. Which Patterns Are Interesting?—Pattern Evaluation Methods
6.4. Summary
6.5. Exercises
6.6. Bibliographic Notes
7. Advanced Pattern Mining
7.1. Pattern Mining: A Road Map
7.2. Pattern Mining in Multilevel, Multidimensional Space
7.3. Constraint-Based Frequent Pattern Mining
7.4. Mining High-Dimensional Data and Colossal Patterns
7.5. Mining Compressed or Approximate Patterns
7.6. Pattern Exploration and Application
7.7. Summary
7.8. Exercises
7.9. Bibliographic Notes
8. Classification
8.1. Basic Concepts
8.2. Decision Tree Induction
8.3. Bayes Classification Methods
8.4. Rule-Based Classification
8.5. Model Evaluation and Selection
8.6. Techniques to Improve Classification Accuracy
8.7. Summary
8.8. Exercises
8.9. Bibliographic Notes
9. Classification
9.1. Bayesian Belief Networks
9.2. Classification by Backpropagation
9.3. Support Vector Machines
9.4. Classification Using Frequent Patterns
9.5. Lazy Learners (or Learning from Your Neighbors)
9.6. Other Classification Methods
9.7. Additional Topics Regarding Classification
9.9. Exercises
9.10. Bibliographic Notes
10. Cluster Analysis
10.1. Cluster Analysis
10.2. Partitioning Methods
10.3. Hierarchical Methods
10.4. Density-Based Methods
10.5. Grid-Based Methods
10.6. Evaluation of Clustering
10.7. Summary
10.8. Exercises
10.9. Bibliographic Notes
11. Advanced Cluster Analysis
11.1. Probabilistic Model-Based Clustering
11.2. Clustering High-Dimensional Data
11.3. Clustering Graph and Network Data
11.4. Clustering with Constraints
11.6. Exercises
11.7. Bibliographic Notes
12. Outlier Detection
12.1. Outliers and Outlier Analysis
12.2. Outlier Detection Methods
12.3. Statistical Approaches
12.4. Proximity-Based Approaches
12.5. Clustering-Based Approaches
12.6. Classification-Based Approaches
12.7. Mining Contextual and Collective Outliers
12.8. Outlier Detection in High-Dimensional Data
12.9. Summary
12.10. Exercises
12.11. Bibliographic Notes
13. Data Mining Trends and Research Frontiers
13.1. Mining Complex Data Types
13.2. Other Methodologies of Data Mining
13.3. Data Mining Applications
13.4. Data Mining and Society
13.5. Data Mining Trends
13.6. Summary
13.7. Exercises
13.8. Bibliographic Notes
Bibliography
Index
Front Matter
Data Mining
Third Edition
The Morgan Kaufmann Series in Data Management Systems (Selected Titles)
Joe Celko's Data, Measurements,